Replication Archive
"Do Voters Discount Political Scandals Over Time?"

Miguel M. Pereira, WUSTL, m.pereira@wustl.edu
Nick Waterbury, WUSTL, nwaterbury@wustl.edu

Version: July 25, 2018

The replication file includes:
1) Scandals data: rep_scandalsdisc.csv
2) Google trends data: trends.csv
3) Replication code: rep_code.R

To replicate the results:
1) Open rep_code.R
2) Set the working directory to location of rep_scandalsdisc.csv and trends.csv
3) Run code

Variables' description in scandals dataset:
nchqual -> Challenger quality: 1 - previously held office; 0 - otherwise
chexp -> Challenger campaign expenses in dollars
inexp -> Incumbent campaign expenses in dollars
timing2 -> Number of months between scandal outbreak and next election (0 if legislators not involved in scandal)
incpvote -> Incumbent's two-party vote share
incpvotelag -> Incumbent's two-party vote share in previous election
anysc -> 1 - Legislator involved in scandal in the current term; 0 - otherwise
scandalEY -> 1 - Scandal outbreak took place in Election year; 0 - otherwise
scandalFY -> 1 - Scandal outbreak took place in first year; 0 - otherwise
open -> 1 - Open seat; 0 - Occupied seat
FINscandalEY -> 1 - Financial scandal in election year; 0 - otherwise
FINscandalFY -> 1 - Financial scandal in election year; 0 - otherwise
CORscandalEY -> 1 - Corruption scandal in election year; 0 - otherwise
CORscandalFY -> 1 - Corruption scandal in election year; 0 - otherwise
SEXscandalEY -> 1 - Sex scandal in election year; 0 - otherwise
SEXscandalFY -> 1 - Sex scandal in election year; 0 - otherwise
POLscandalEY -> 1 - Political scandal in election year; 0 - otherwise
POLscandalFY -> 1 - Political scandal in election year; 0 - otherwise
OTscandalEY -> 1 - Other scandal in election year; 0 - otherwise
OTscandalFY -> 1 - Other scandal in election year; 0 - otherwise
scandalq1-8 -> 1 - Scandal outbreak 1-8 quarter(s) from election; 0 - otherwise
sexscandal -> 1 - Sex scandal; 0 - otherwise
corruption -> 1 - Corruption scandal; 0 - otherwise
finscandal -> 1 - Financial scandal; 0 - otherwise
polscandal -> 1 - Political scandal; 0 - otherwise
altscandal -> 1 - Other scandal; 0 - otherwise

Variables' description in google trends dataset:
Note: dataset includes same-state legislators involved in scandals during the same term. Each row represents one dyad.
SexVS -> 1 - Dyad involves at least one sex scandal; 0 - otherwise
OverallMax1-2 -> Relative volume of interest throughout the term for legislator 1-2
MonthMax1-2 -> Relative volume of interest in the month of the scandal outbreak for legislator 1-2
MaxDiff2 -> Difference in relative volume of interest
fin_sex -> 1 - Financial and sex scandals; 0 - otherwise
pol_sex -> 1 - Political and sex scandals; 0 - otherwise
sex_other -> 1 - Sex and other scandals; 0 - otherwise
fin_other -> 1 - Financial and other scandals; 0 - otherwise
fin_pol -> 1 - Financial and political scandals; 0 - otherwise
pol_pol -> 1 - Political and political scandals; 0 - otherwise
State - Factor variable identifying the legislators' state